Access to care

library(tidyverse)

-- Attaching packages --------------------------------------- tidyverse 1.2.1 --
v ggplot2 3.1.0       v purrr   0.3.1  
v tibble  2.0.1       v dplyr   0.8.0.1
v tidyr   0.8.3       v stringr 1.4.0  
v ggplot2 3.1.0       v forcats 0.4.0  
package 㤼㸱purrr㤼㸲 was built under R version 3.5.3-- Conflicts ------------------------------------------ tidyverse_conflicts() --
x dplyr::filter() masks stats::filter()
x dplyr::lag()    masks stats::lag()

library(leaflet)

package 㤼㸱leaflet㤼㸲 was built under R version 3.5.3

hospitals <- read_csv("../data/hospital_list.csv")

Parsed with column specification:
cols(
  .default = col_character(),
  zip = col_double(),
  `Phone Number` = col_double(),
  score = col_double(),
  longitude = col_double(),
  latitude = col_double(),
  population = col_double(),
  poverty = col_double(),
  percent = col_double(),
  poverty_percent = col_double()
)
See spec(...) for full column specifications.

hospitals

set.seed(100)

hospital_sample <- state_hospitals %>%
  sample_frac(0.3)

hospital_sample

summary(model)

Call:
lm(formula = hospitals ~ population, data = hospital_sample)

Residuals:
     Min       1Q   Median       3Q      Max 
-10.5571  -0.2355  -0.0480   0.0686   8.3029 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) 9.068e-01  3.764e-02   24.09   <2e-16 ***
population  7.854e-06  8.055e-08   97.52   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9844 on 737 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.9281,    Adjusted R-squared:  0.928 
F-statistic:  9509 on 1 and 737 DF,  p-value: < 2.2e-16

predictions <- predict(model, 
  newdata = state_hospitals, 
  interval = "prediction") %>%
  as_tibble() %>%
  mutate_all(round)

predictions

leaflet() %>%
  addTiles() %>%
  addPolygons(data = state_shapes, weight = 0.5, color = prep_colors$color)

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